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5th Semester Mechanical

NAME: ___________________
BATCH: __________________
ROLL NO: _______________
REGISTER NUMBER: ______________________

Metrology and Instrumentation Lab

BATCH NO: ________

Name of the experiment

ROLL NO: _______

Page No.

Measurement of lengths,
heights, diameters by
Height Gauge
Measurement of lengths,
heights, diameters by
Vernier Calipers
Introduction and
Measurement with Slip
Composite Error
measurement from
Parkinson Gear Tester
Measure the Major,
Minor and Effective
diameter of external
parallel screw threads
using Floating Carriage
Measurement of screw
thread using tool makers
Measurement using
Profile Projector
Experiment to determine
area of irregular surface
by planimeter
Introduction and
Measurement with
Optical Comparator
(Least Count 0.002 mm)
Measurement WITH Gear
roller Tester
To Perform Calibration
test of Pressure Gauge
Introduction to GO and
NO-GO plug gauges
Introduction to
temperature measurement
Introduction and
Measurement with Screw

Thread Micrometer
Final internal marks awarded (out of 60)

AIM: - To measure lengths, heights & diameters by height gauge
Instruments used1. Surface 2. Vernier height gauge 3. Specimens
Specification: Range 400 mm
Least Count 0.02 mm
TheoryVernier height gauge is a sort of Vernier calipers equipped with a special with a base and other
attachment, which make the instrument suitable for height measurement. Along with sliding jaw
assembly arrangement is provided to carry a removable clamp. The upper and lower surfaces of
the measuring jaws are parallel to base, so that it can be used for measurements over or under
surfaces. The Vernier height gauge is mainly used in the inspection of parts and layout work. The
Vernier height gauge can be used to scribe lines at certain distance above surface with a scribing
attachment in pace of measuring jaw. Dial indicators can also be attached in the clamp and many
exact measurements can be made as it exactly gives the indication when the dial tip is touching
the surface. Surface plates as datum surface are used for the above measurements.

Nomenclature of parts of Height Gauge

Procedure1. Place the object and the vernier height gauge on the surface plate.
2. Note the value on the scale when the moving jaw is touching the bottom of the
3. Take the moving /sliding jaw to the top of the object and note down the value
on the scale.
4. The difference between 3&2 will give the height of the object.

Main Scale Reading, Vernier Scale Reading Measured reading=

MSR (mm)
VSR (mm)
= mm

1. The height gauges should be kept in their case when not in use.

2. Measuring jaws should be handled carefully.

Aim- Measurement of lengths, heights, diameters by Vernier Calipers
Theory-The Digital Caliper (sometimes incorrectly called the Digital Vernier Caliper) is a
accurately. Earlier versions of this type of measuring instrument had to be read by looking
to read the small sliding scale. The digital version requires a small battery whereas the
manual version does not need any power source. Digital calipers are easier to use as the
be read as metric or imperial.

Vernier Caliper

Measurement in (mm)

Result:Procedure1. The display is turned on with the on/off button.

2. The external jaws should then be brought together until they touch and the zero button
should be pressed.
3. The material to be measured is placed between the external jaws and they are carefully
brought together.
4. The locking screw is tightened so that the jaws do not move apart.
5. The digital display can then be read.

Aim- Introduction and measurement with Slip Gauge
Theory- Slip gauges are measuring blocks of hardened steel used for measurement, testing
and calibration. Various dimensions can be built up by wringing phenomenon. Slip gauges
are also called as JOHANSON GAUGES. The measuring surface of gauges is perfectly flat
and accurate, so that any two faces may be wrung together.
As per IS: 2984-1966, slip gauges are available in three different grades i.e. grade O, grade I,
and grade II.
Grade I Slip gauges are used in inspection department
Grade II Slip gauges are used in workshop
Grade 0 Slip gauges are used in laboratories & standard rooms
Some available sets of slip gauges are M112, M105, M50, M33, M27, M45, etc.
Wringing phenomenon:
When two clean & perfectly flat surfaces are slide together under pressure, they adhere
firmly. This phenomenon is called as WRINGING of slip gauges. The phenomenon of
wringing occurs due to molecular adhesion between liquid film & the mating surface.

Wringing of Slip Gauges

Procedure1. Calculate and select the minimum number of slip gauges required for building up the
dimension, from the given set of slip gauges.
2. Take out the required slip gauge from the box & keep it on cleaned surface plate.
3. Clean all the surfaces of the selected slip gauges using petrol or kerosene & wipe it with a
soft cloth & chamois leather.
4. Place two slip gauges together at right angles to each other to form a cross shape.
5. Rotate any one slip gauge through 90 degree while applying a small amount of pressure.
6. The slip gauges will get wrung together.
7. Wring all the slip gauges together as shown in Fig. and build the required dimensions.

8. Use this combination for the stated purpose, if any.

9. Slowly remove all the slip gauges.
10. Properly clean & put the slip gauges in wooden box at appropriate place.

Observation table-

Precautions1. The surface of gauge must be covered with natural petroleum jelly or some anticorrosive
to avoid rusting.
2. The gauges should be used under controlled condition of temperature.
3. Wringing of gauges should be done without applying undue pressure to avoid damage
to the surface.
4. Never drop slip gauges.
5. Never strike slip gauges with other metallic objects.
6. Use minimum number of gauges for building up size combination.

Aim:To measure composite error with Parkinson Gear Tester
TheoryThe principle of this device is to mount a standard gear on a fixed vertical spindle and the gear to
be tested on another similar spindle mounted on a sliding carriage, maintaining the gears in mesh
by spring pressure. Movements of the sliding carriage as the gears are rotated are indicated by a
dial indicator, and these variations are a measure of any irregularities in the gear under test;
alternatively a recorder can be fitted, in the form of a waxed circular chart and records made of
the gear variation in accuracy of mesh.
The gears are mounted on the two mandrels, so that they are free to rotate without measurable
clearance. The left spindle can be moved along the table and clamped in any desired position.
The right mandrel slide is free to move, running on steel balls, against spring pressure and it has
a limited movement. The two mandrels can be adjusted so that their axial distance is equal to the
designed gear center distance.

Parkinson gear tester

The spring pressure can be regulated. There are also screws for limiting the movement of the
sliding carriage. A scale is attached to one carriage and a Vernier to the other; this enables center
distances to be measured to within 0.025 mm. The dial indicator on the right contacts the right
end of the sliding carriage and therefore indicates any radial variations of the gear under test as
the gears are rotated.
When the waxed paper recorder is fitted, the chart makes a revolution for each one of the gears
mounted on the sliding carriage. As the chart moves or rotates, the line traced records the
movements of the floating carriage, a circle is drawn at the same time as the record. The figures
shown in are reproduction of a few typical charts with a reduced scale and the radial errors
magnified about 50 times. The gear shown by No. 1 record is a fully satisfactory one, that at No.
2 is a moderate gear at No. 3 is an unsatisfactory one.

Observation tableCentre Distance (mm)

Composite error (mm)

Precautions:1. Keep the instrument away from acid vapors ,oil, dust.
2. Keep the instrument clean & apply petroleum jelly over the surface when it is not used.
3. When required clean it with tissue paper & petrol, just before use.

Aim:To measure the Major, Minor and Effective diameter of external parallel screw threads using
Floating Carriage Micrometer
TheoryIn order to ensure the manufacture of screw threads to the specified limits laid down in the
appropriate standard it is essential to provide some means of inspecting the final product. For
measurement of internal threads thread plug gauge is used and to check these plug gauges
Floating Carriage Micrometer is used for measuring Major, Minor and Effective diameter.

Floating Carriage Micrometer

Procedure1. The diameter of setting master or a cylinder should be nearly same as the diameter of the
thread gauge to reduce error.
2. First meet the anvil of micrometer to dial gauge and rotate micrometer in forward to

rotate needle in dial by 10mm with setting its scale zero at needle position and make digital
micrometer to zero by pressing button.
3. Put master in between centers and make dial needle to zero by moving micrometer.
Take the reading on micrometer as RS.. Similarly replace master with a threaded work piece
again second reading is taken as R
4. Hang the required set of prisms (from Appendix-1 according to pitch of work piece)
on the hooks provided on both sides of standard. Take the reading on micrometer and zero
indicator in such a way that portion of prism touches master and flat portion of prism is on
micrometer & zero indicator.
5. Take reading for master as RP and work piece as Rq on micrometer as needle on dial
indicating zero.
6. Now place two small wires or cylinders in place of prism. The wires should be
chosen so that, when placed between the threads, they should contact about halfway down the
flanks. Suitable sizes of these cylinders for various threads are given atAppendix-1
7. Take the readings for master as RW and work piece as ROW on micrometer as needle on dial
indicating zero.
Calculation1. Two Wire MethodThe effective diameter of a screw thread may be ascertained by placing two wires or rods of
identical diameter between the flanks of the thread, and measuring the distance over the
outside of these wires. The effective diameter is then calculated as
Where T= Dimension under the wires =M2d
M=dimension over the wires, d= diameter of each wire
The wires used are made of hardened steel to sustain the wear and tear in use. These are given
a high degree of accuracy and finish by lapping to suit different pitches. Dimension T can also
be determined by placing wires over a standard cylinder of diameter greater than the diameter
under the wires and noting the reading R1 and then taking reading with over the gauge, say
Then T=S(R1R2).
P=It is a value which depends upon the diameter of wire and pitch of the thread.
If P= pitch of the thread, then
P= 0.9605p1.1657d (for Whitworth thread).
P= 0.866pd (for metric thread).
To give the effective diameter, The expression for the value of P in terms of p (pitch), d(diameter
of wire) and x (thread angle) can be derived as follows:
In Fig. since BC lies on the effective diameter line
BC= pitch= p
OP= (d cosec x/2)2
PA=d (cosecx21) 2
PQ=QC cot x2=p4 cot x2
AQ=PQAP=(p cot x2)4 d (cosec x2 1)2
AQ is half the value of P
.. P value=2AQ =p2 cot x2 d (cosecx21)

Two wire method can be carried out only on the diameter measuring machine described for
measuring the minor diameter, because alignment is not possible by two wires and can be
provided only by the floating carriage machine. In the case of three wire method, two wire, on
one side help in aligning the micrometer square to the thread while the third placed on the other
side permits taking of readings.
2. Three Wire MethodThis method of measuring the effective diameter is an accurate method. In this three wires or
rods of known diameter are used; one on one side and two on the other side This method
ensures the alignment of micrometer anvil faces parallel to the thread axis. The wires may be
either held in hand or hung from a stand so as to ensure freedom to the wires to adjust
themselves under micrometer pressure.
M=distance over wires E=effective diameter
r=radius of the wires d=diameter of wires
h =height of the center or the wire or rod from the effective
x=angle of thread.
AD = AB cosec x2 = r cosec x2
H = DE cot x2 = p2 cot x2
CD = H = p4 cot x2
r = cosec x2 p4 cot x2
Distance over wires=M = E+2h+2r
= E+2(r cosec x2 p4 cot x2)+2r
= E+2r (l+cosecx2 ) p2 cot x2
or M = E+d (1+cosec x2) p2 cot x2
(since 2r = 0 )
(i) In case of Whitworth thread:
X = 55, depth of thread = 0.64 p, so that
E= D0.64 p and cosec x2 = 2.1657
Cot x2 = 1.921
M = E+d(1l+cosec x2) p2 cot x2
= D0.64p+d(1+2.1657)p2(1.921)
= D+3.1657d1.6005p
M = D+3.1657d1.6p

Observation table-

Pitch circle diameter(PCD)

Outer diameter(OD)
Inner diameter(ID)

AimMeasurement with Tool Makers Microscope.
TheoryThe tool makers microscope is a versatile instrument that measure by optical means with no
pressure being involved, thus very useful for measurement on small and delicate parts.
It is designed for:
a) Measurement on parts of complex form e.g. - profile of external thread, tool, templates,
gauges, etc.
b) Measuring center to center distance of holes in any plane.
c) A variety of linear measurements.

Tool Makers Microscope

Tool makers microscope is shown in figure. The optical head can be moved up or down the
vertical column and can be clamped at any height by means of clamping screw. The table which
is mounted on the base of the instrument can be moved in two mutually perpendicular horizontal
directions (longitudinal and lateral) by means of accurate micrometer screw having thimble scale
and venires.

PrincipleA ray of light from a light source is reflected by a mirror through 90 It then passes through a
transparent glass plate (on which flat parts may be placed). A shadow image of the outline or
counter of the workspaces passes through the objective of the optical head and is projected by a
system of three prisms to a ground glass screen.
Observations are made through an eyepiece. Measurements are made by means of cross lines
engraved on the ground glass screen. The screen can be rotated through 360; the angle of
rotation is read through an auxiliary eyepiece.

Principe of Tool Makers Microscope

Procedure Pitch Measurement1) Take the hacksaw blade and mount on the moving blade of tool makers Microscope in
horizontal position.
2) Focus the microscope on the blade.
3) Make the cross line in the microscope coincided with one of the edge of the blade.
4) Take a reading on ground glass screen, this is the initial reading.
5) The table is again moved until the next edge of the blade coincides with the cross-line on the
screen and the final reading takes.
6) The difference between initial and final reading gives pitch of the blade.

Thread Angle-

1) Place the blade on the table in same position.

2) Rotate the screen until a line on the angle of screen rotation is noted.
3) Take the angular reading, the initial one.
4) Again rotate the screen until the same line coincides with the other flank of the tooth.
5) Take the final angular reading.
6) The teeth angle of blade in the difference between the two angular readings.

Observation table-

Aim:To measure screw thread measurement and screw thread profile with profile projector
Profile Projector:Profile projector is a measuring machine which displays a magnified shadow of the opaque parts
being measured on a screen where its dimensions are measured or compared with a master
drawing. Using a profile projector, measurement of small sized complicated shaped components
can be easily carried out.
Profile projector consists of four essential elements:1)

Source of light
Collimating or condensing lens
Projection lens
Ground glass.

The illumination system consists of a light source. Light from the lamp passes through a system
of lenses called achromatic condenser. The parallel beam of light from the condenser is
transmitted to the illumination mirror which bends it vertically upward through the class stage
plate in the worktables of the projector.
Projection system consists of projection lens, roof prism, a pair of image reflectors and screen.
Magnification of projector can be adjusted by changing projection lens assembly. The light
travels past the object to the projection lens, from which its passes upwards to roof prism.
Function of roof prism is to direct the beam of light horizontally towards the back of the
projector to assist the projection of image so that its aspects as presented to the observer are
correct. From roof prism light passes to two optically flat reflectors which change the direction
of beam of light and direct it horizontally to the screen.
Illumination section: For contour examination transmitted light [DIA-Diascopic] and for
surface examination indirect light [EPI-Epidoscopic].

1) For corrects measurement object should be kept perfectly horizontal and perfectly
focused on the screen.
2) For surface examination EPI lamp should be kept very close to the object and it
should make minimum possible angle with the lens tube.
OBSERVATION TABLE :1) Measurement of bore diameter

Initial reading (mm)

Final reading (mm)

Bore diameter (mm)

2) Screw thread measurement

reading reading rence

3) Comparison
Basic size:
Object no

Conclusion (Reject

Results:Bore diameter = --------------------Outer diameter = --------------------Core diameter = --------------------Pitch

a) Ind. = --------------------b) Span = ---------------------

Aim:To measure area of irregular surface by planimeter
TheoryPlanimeter is a Surveying instrument which can be used to find out the area of a map or plan. A
Swiss mathematician Jacob Amsler Laffon built first modern planimeter in 1854. Many
developments followed Amsler's famous Planimeter including the electronic planimeter.

We have three mechanical types of Planimeters:
Hatchet Planimeter
All Planimeter work on the same principle. There is a linkage, at one end is a pointer and the
other is fixed for a polar planimeter and is restricted for a linear. The pointer is traced along the
perimeter of the area of the shape is indicated on a scale.

ProcedureThere is a wheel attached to planimeter which rolls when the movement of the pointer is
perpendicular to the axis of the wheel so, this movement is recorded.It will skid when the
movement of the wheel is parallel to the axis so, this movement is ignored. That means that
planimeter measures the distance which is traveled by the rolling of the wheel. Area of the shape
is proportional to the nos. of turns through which the measuring wheel turns. In order to find out
the area, all you have to do is to trace the perimeter of the area on the map, and note down the
reading. To get more accurate results one should be skilled in tracing the exact perimeter of the
shape. You can convert the area to the field area using the scale of the map.

Observation tableArea obtained from Planimeter

Scale * Area

AimMeasurement by optical comparator

Specification1. Base steel plate of 200x280mm
2. Vertical slide capacity 100mm travel
3. Light source input 5mp 230 v with 1w led
4 work base die 50mm hardened ground base
5. Plunger travel 0.1 mm on either side
6. Optical scale 0 to 0.1 mm either side

Optical Comparator
Procedure1. To start place a master gauge block nearest to the sizes of work piece under test on the work
base of the comparator.
2. Switch on the light source at the top of the comparator unit.
3. Using knob provided at the top of the vertical slide, bring the unit downwards until the plunger
comes in contact with the master /work piece
4. When the plunger in contact and with little initial pressure on it, a image of cross wire appears
at bottom of the illuminated scale on the front of the comparator unit.
5. Planning master under plunger set the cross wire at 0 (zero) position at the center of the
6. This scale has division marked as 0.02mm up to 0.1 mm above and below zero marking
7. Now smoothly slide work piece under the plunger so that cross defect. If the work piece is so
of lower size than master cross moves below zero mark that is - side and if larger then above
zero mark that is ++ side

8. Scale marking to which horizontal line of cross is coinciding indicates difference in size from
that of master.

Observation tableS.no.

Std Piece Size

The core moves up or down, the induced voltage of one secondary coil increases while that of
other decreases the output voltage which is modulated, is the difference of the two, since
secondarys are in opposition. The output is proportional to the displacement of the iron core.
The features of LVDT are:
1) Due to no physical contact between the core and coil its mechanical components do not
wear out: as a result the friction is absent and true infinite resolution with no hysteresis is
2) The small core mass and the lock of friction enhance response capabilities for dynamic
measurements, and thus it becomes very suitable for taking measurement for on-line
3) Further as it is not affected by overload, its reliability is high.
Result:Precautions1. When the instruments is not in the use , cover it by plastic cover.
2. Keep the instrument away from acid vapors ,oil, dust
3. Do not touch the screen glass, worktable glass, lenses, mirrors, by naked hand to avoid the
4. Clean the work table glass by tissue paper and coiling.
5. Always keep the instruments clean and dry
6. Handle Micrometer Gently.

AIM:To measure deviation in CD and composite error in gear
TheoryWith gear roll bench following test on gear possible
1. Checking Deviation actual CD & Theoretical CD
2. Gear roll test with a Master Gear which gives composite error in gear.
Procedure1. Engage the gear under test in slide A. Engage the master gear in side B. Move the slide A till
there is no play between the two gear.
2. The master gear is Mounted on side B is floating on Balls & is free to move to & fro .small
spring in the side B will press the gear against the gear under test.
3. Lock the slide to the main guide bar .adjust the plunger dial gauge (2) with the little pressure
(appx 1mm ) reading and lock it.
4. Now rotated the gear under test slowly. The plunger dial gauge will show you the variations
.the displacement of the plunger dial gauge arm is indication of
Gear profile.
Concentricity of the gear (RUN OUT)
Observation tableS.no.

Run Out

Depth (mm)

Results: -

Precautions1. Keep the instrument away from acid vapors ,oil, dust.

AIM:Calibrate Pressure Gauge
TheoryPressure gauges are one of the most commonly used and relied upon instruments in todays
manufacturing environment. However, due to their great numbers, there is often a lack of
attention given to pressure gauge calibration intervals and therefore reliability can become
compromised. Due to these oversights, it is not uncommon to see many pressure gauges out of
calibration, tagged out of service or not functional at all. This can become a real problem because
if a plant is operating with a failed or out of calibration pressure gauge in a critical process, the
quality of the product being produced or the safety of the plant may be at risk. Therefore, one
goal of good process control is to establish a procedure to set and ensure pressure gauge
calibration intervals are maintained. There are many different types of pressure gauges used in
almost every manufacturing environment imaginable.

Pressure gauge
ProcedureCalibration consists of two parts
Full scale adjustment (steps 1 5)
Verification of linearity (step 6 - 7)
This procedure assumes the user has a comparator-type pressure reference, and can generate the
required pressures accurately. It also assumes that the user is familiar with pressure gauge safety
and testing recommendations in ASME B40.1
UUT refers to the Unit Under Test (the gauge being calibrated).
REF refers to the reference gauge.
100% refers to 100% of gauge scale; 10% refers to 10% of gauge scale; etc.

1. Mount the pointer.

If UUT has a stop pin, mount pointer at 10%, with pressure at 10% of scale.
If UUT has no stop pin, mount pointer a zero, with no pressure on UUT.
2. Apply pressure so UUT reads 100%. Read actual REF pressure. Note if UUT is reading high
or low, and degree of variation.
3. Remove pressure, and remove pointer and dial (if necessary).
4. Adjust full scale slide or gooseneck:
If REF was greater than UUT, reduce distance between pushrod and shaft.
If REF was less than UUT, increase distance between pushrod and shaft.
5. Mount dial and pointer. Repeat steps 1 4 until UUT reads within specified accuracy with
respect to REF when at 100%.
6. Apply 25%, 50%, 75%, and 100% to UUT. At each point, compare UUT to REF. If all values
are within specified limits, slowly reduce pressure and check descending pressure at 75%, 50%,
and 25%. If any intermediate readings are out of limits, perform step 7. If 100% reading was out
of limits, go back to step 1. If all readings are within limits, calibration is finished.
7. Remove pressure from UUT, and remove pointer and dial. Adjust pushrod (linearity
adjustment) as follows:
If REF was reading higher than UUT in step 6, lengthen the pushrod (this may require
loosening and sliding, or bending).
If REF was reading lower than UUT in step 6, shorten the pushrod (this may require
loosening and sliding, or bending).
8. Repeat steps 1 6.
If UUT and REF agree within accuracy limits, calibration is finished.
If UUT and REF disagree by more than accuracy limits, go back to step 3.
9. Note: If linearity cannot be achieved with the above procedure, other factors (such as dial
centering, mechanical friction in movement) may need to be addressed).
Observation Table:UUT READING

AIM:Introduction about GO and NOGO Gauge
A Go-No GO gauge refers to an inspection tool used to check a work piece against its allowed
tolerances. It derives its name from its use: the gauge has two tests; the check involves the work
piece having to pass one test (Go) and fail the other (No Go). It is an integral part of the quality
process that is used in the manufacturing industry to ensure interchangeability of parts between
processes, or even between different manufacturers. A Go - No Go gauge is a measuring tool that
does not return a size in the conventional sense, but instead returns a state. The state is either
acceptable (the part is within tolerance and may be used) or it is unacceptable (and must be
They are well suited for use in the production area of the factory as they require little skill or
interpretation to use effectively and have few, if any, moving parts to be damaged in the often
hostile production environment.
Gauges are inspection tools which serve to check the dimensions of the manufactured parts.
Limit gauges ensure the size of the component lies within the specified limits. They are nonrecording and do not determine the size of the part. Plain gauges are used for checking plain
(Unthreaded) holes and shafts. Plain gauges may be classified as follows; According to their
type: (a) Standard gauges are made to the nominal size of the part to be tested and have the
measuring member equal in size to the mean permissible dimension of the part to be checked. A
standard gauge should mate with some snugness. (b) Limit Gauges These are also called go and
no go gauges. These are made to the limit sizes of the work to be measured. One of the sides or
ends of the gauge is made to correspond to maximum and the other end to the minimum
permissible size. The function of limit gauges is to determine whether the actual dimensions of
the work are within or outside the specified limits. According to their purpose: (a)Work shop
gauges: Working gauges are those used at the bench or machine in gauging the work as it being
made. (b)Inspection gauges: These gauges are used by the inspection personnel to inspect
manufactured parts when finished. (c) Reference or Master Gauges: These are used only for
checking the size or condition of other gauges. According to the form of tested surface: Plug
gauges: They check the dimensions of a hole Snap & Ring gauges: They check the dimensions of
a shaft. According to their design: Single limit & double limit gauges Single ended and double
ended gauges 22 Fixed & adjustable gauges LIMIT GAUGING Limit gauging is adopted for
checking parts produced by mass production. It has the advantage that they can be used by
unskilled persons. Instead of measuring actual dimensions, the conformance of product with
tolerance specifications can be checked by a GO and NO GO gauges. A GO gauge
represents the maximum material condition of the product (i.e. minimum hole size or maximum

shaft size) and conversely a NO GO represents the minimum material condition (i.e. maximum
hole size or minimum shaft size)

Plug gaugesPlug gauges are the limit gauges used for checking holes and consist of two cylindrical wear
resistant plugs. The plug made to the lower limit of the hole is known as GO end and this will
enter any hole which is not smaller than the lower limit allowed. The plug made to the upper
limit of the hole is known as NO GO end and this will not enter any hole which is smaller than
the upper limit allowed. The plugs are arranged on either ends of a common handle.

Materials used for gauges:

High carbon steel:
Heat treated Cast steel (0.8-1% carbon) is commonly used for most gauges.
Mild Steel: Case hardened on the working surface. It is stable and easily machinable.
Case hardened steel: Used for small & medium sized gauges.
Chromium plated & Hard alloys: Chromium plating imparts hardness, resistance to abrasion &
corrosion. Hard alloys of tungsten carbide may also be used.
Cast Iron: Used for bodies of frames of large gauges whose working surfaces are hard inserts of
tool steel or cemented carbides.
Glass: They are free from corrosive effects due to perspiration from hands. Also they are not
affected by temperature changes.
Invar: It is a nickel-iron alloy (36% nickel) which has low coefficient of expansion but not
suitable for usage over long periods. (The name, Invar, comes from the word invariable, referring
to its lack of expansion or contraction with temperature changes. It was invented in 1896 by
Swiss scientist Charles Eduard Guillaume. He received the Nobel Prize in Physics in 1920 for
this discovery, which enabled improvements in scientific instruments.)
Taylors Principle of Gauge Design: According to Taylor, Go and No Go gauges should be
designed to check maximum and minimum material limits which are checked as below
GO Limit. This designation is applied to that limit of the two limits of size which corresponds
to the maximum material limit considerations, i.e. upper limit of a shaft and lower limit of a hole.
The GO gauges should be of full form, i.e. they should check shape as well as size.

No Go Limit: This designation is applied to that limit of the two limits of size which
corresponds to the minimum material condition. i.e. the lower limit of a shaft and the upper limit
of a hole. No Go gauge should check only one part or feature of the component at a time, so
that specific discrepancies in shape or size can be detected. Thus a separate No Go gauge is
required for each different individual dimension.

AIM:To Measure Temperature with Thermocouple
Fundamental laws
Seebeck Voltage When two dissimilar metals are joined at one end, an electrical potential called
the Seebeck voltage is generated, which changes proportionally to changes in the temperature
at the joint.

Where is called the Seebeck coefficient which is, in general, a nonlinear function of
A. Law of Intermediate MetalsThe introduction of a third metal (Metal C) between two other dissimilar metals (Metal A and B)
will yield the same Seebeck voltage, VAB, obtained by removing Metal C, if all the joints are at
the same temperature.

B. Sensor ConfigurationThe simplified cold-junction configuration is given

Let the meter reading be V (in millivolts) and TCJ (cold junction temperature) be measured in
C. Then the temperature T can be found by using the following equations:

Experimental Setup

Reading Table:S.No.

AIM: To Measure the effective diameter of the given screw thread by

a) Using Screw thread micrometer
DESCRIPTION:Effective diameter or pitch diameter is the diameter of pitch cylinder (the Imaginary cylinder
which is co axial with the axis of the screw and intersects the flanks of the threads in such a
way as to make the width of the thread and width of the space between the threads equal).
The effective diameter is equal to the length of the axis and measured between the points
where it cuts the flank of the thread.
a) Measurement of effective diameter using thread micrometer:
In the micrometer the end of the spindle is pointed to V thread form with corresponding
V recess in the fixed anvil. Suitable contact points are selected according to pitch of the
thread and fixed to screw thread micrometer and the pitch diameter or effective diameter is
read directly on the micrometer.
Measurement of Effective diameter using screw thread micrometer:Trail No.

Size of anvil used

Effective diameter


Informative speech thesis outline

All cells must contain text. This will form the heart of your thesis. An effective statement will express one major idea.

Informative speech thesis outline

Outlines your main points, with subpoints and supporting facts Includes transitions between main points The conclusion: Signals the end is coming Restates your thesis Ends strongly, but quickly, wrapping it up and driving your point home You are likely already familiar with the basic outline formatwhich uses Roman numerals for the introduction, body main pointsand conclusion, and letters and numbers for subpoints and supporting facts.

Get ahead in your homework. Click here to learn more. For example, a speech outline may include a notation about visual aids you are using or when to emphasize using gestures or pauses, which would not be necessary in an informative essay outline.

Again, the key parts of your outline will remain the same, but the pattern of organization you use in your outline may vary based on which type of informative speech you are writing. Types of informative speeches include: A preparation outline is typically full sentences and is used to help write your speech.

A speaking outline uses keywords and phrases, generally on note cards, and is designed to jog your memory during your speech, keeping you on track, without distracting you from delivering your message.

Informative Speech outline template - Google Docs

Informative speech outline examples and tools 1. Demonstration speech with chronological organization This informative outline example from Santa Monica College is a full sentence outline of a demonstrative, or how to, informative speech about CPR.

It uses a chronological pattern of organization, describing steps in the order you would perform them. The body consists of three main points, each with three to four subpoints.

Informative speech thesis outline

The introduction shows a clearly defined thesis statement. Process informative speech outline examples using phrases An example of a process informative speech outlinethis outline informs how to eat healthy with a busy lifestyle.

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The pattern of organization is topical rather than chronological and this outline uses phrases, rather than full sentences, showing an alternative to the complete sentence speech outline. If you have a brief speech to give or are confident in your abilities to prepare and deliver your speech, this outline may suit your needs.

A full-sentence process speech outline in pdf formatthis outline describes the process of spray paint art. It shows how to incorporate a visual aid in the introduction. It also shows how to use transitions between main points in your outline. Complete sentence outline example This informative process speech outline demonstrates how to create a personal recipe collection cookbook.

From the labeled anecdote in the introduction to the transitions between sections, it is a great example of a complete sentence outline and is a Word document so the content could easily be replaced, while keeping the basic format to be used for any topic.

Topical pattern of organization outline example From the Monmouth College, this outline is an example of the most commonly used pattern of organizationtopical. It also shows the effective use of internal summaries and internal transitions between sections for a smooth speech.

Event informative speech outline examples This full sentence event informative speech outline about the sinking of the Titanic is from Central Michigan University. It shows an effective attention getter and credibility statement in the introduction, as well as transitions between sections.

This outline is downloadable as a Word document and provides an example of an event informative speech outline describing the Black Sox Scandal. It uses full sentences and then shows how to use a central idea as a thesis statement.

In addition, it shows how to develop your points in the body paragraph, starting with a broad idea, then a supporting statement, then specific details. Object informative speech preparation outline and speaking outline This is an assignment used at Bakersfield College.

Informative speech thesis outline

It is downloadable as a Word document, and it includes both an example of a complete sentence object informative speech outline about a person you and also an abbreviated version suitable for a speaking outline.

This outline sample is a comprehensive guide from the University of Hawaii used for developing a preparation outline for your informative speech.

It provides detailed instructions for each part of the outline, including how to cite your sources in MLA or APA format. Concept informative speech outline examples A concept informative speech can be a bit more subjective. This sample outlines an informative speech about the history of Halloween ; however, it also discusses the concept of conformity, making it a good example of this type of informative speech.

Selecting Your Topic

Informative brief outline format This is a short outline example from the University of Delaware that covers the main parts of an informative outline without being overly complex.Video: Developing a Thesis Statement from Your Speech Topic The thesis statement is a vital part of your speech.

In this lesson, learn how to create a thesis statement and identify problems in. An informative speech is a five to six-minute speech that utilizes descriptions, demonstrations, details, and definition to explain a complex topic in an easy-to-understand way.

use the following outline to begin writing your own. Reiterate the points you have discussed in your speech. Restate of Thesis. EXAMPLE OF INFORMATIVE SPEECH OUTLINE Sarah Putnam Informative Outline Topic: The Titanic General Purpose: To Inform Thesis: Even though noise-induced hearing loss can be easily prevented, it is the number one cause of deafness for people of all ages.

Write an informative speech outline by using these basic elements, important for any good speech presentation. THESIS GENERATOR. Thesis Statement Guide Development Tool. Thesis Statement Guide: Sample Outline.

Essay Writing Service #1 | Custom Papers -

This is meant as a guide only, so we encourage you to revise it in a way that works best for you. Again, use the Thesis. of an informative speech is to help your audience to understand and to remember the that support the chosen points and create a working outline with a general and specific purpose statement, a thesis statement, and the three main points with statistics may make your speech more informative, but giving too many statistics can.

Informative Speech outline template - Google Docs


What We Learned: Notre Dame’s upside is clear. But can the Irish get there?

By Pete Sampson Sep 23, 2019 95
SOUTH BEND, Ind. – The season’s first loss always comes with revelations, like them or not.

Notre Dame’s 23-17 defeat at Georgia was no different in that respect, even if the stories emerging from Sanford Stadium trended more toward how close the Irish were to the top of college football’s mountaintop rather than how the program continues to falter in the very last step.

It all makes assessing what Notre Dame did and didn’t do against Georgia complicated. Because even though the Irish lost and the reasons for that are worth unpacking, the ground the program traveled to earn a game-winning shot in the final minutes are worth recognizing, too.

What did we learn on Saturday night? Plenty. But these are three lessons worth understanding before spinning the season forward.

A better Ian Book doesn’t involve his arm

It should be easy to pick apart Ian Book’s performances based on deep throws because they’re so obvious. A quarterback...


Beyond the pixel plane: sensing and learning in 3D

Imagine you're building a self-driving car that needs to understand its surroundings. How would you enable your car to perceive pedestrians, bikers, and other vehicles around it in order to move safely? You could use a camera for this, but that doesn't seem particularly effective: you'd be taking a 3D environment, "squashing" it down into a 2D image captured from the camera, and then trying to recover the 3D information you actually care about (like the distances to pedestrians or cars in front of you) from that 2D image. By squashing your 3D surroundings down to a 2D image, you're throwing out a lot of the information that matters most to you. Trying to piece this information back together is difficult and, even for state-of-the-art algorithms, error-prone.

Instead, you’d ideally be able to augment your 2D view of the world with 3D data. Rather than trying to estimate distances to pedestrians or other vehicles from a 2D image, you would be able to locate these objects directly through your sensor. But now another part of the pipeline becomes difficult: the perception. How do you actually recognize objects like people, bicyclists, and cars in 3D data? Traditional deep learning techniques like convolutional neural networks (CNNs), which would make identifying these objects in a 2D image straightforward, need to be adapted to work in 3D. Luckily, the problem of perception in 3D has been studied quite a bit over the past few years, and our mission in this article will be to give a brief overview of this work.

In particular, we’ll focus on recent deep learning techniques that enable 3D object classification and semantic segmentation. We'll begin by reviewing some background information on common ways to capture and represent 3D data. We'll then describe fundamental deep learning methods for three different representations of 3D data. Finally, we'll describe promising new research directions and conclude with our perspective on where the field is headed.

How do we capture and represent 3D data?

It’s clear that we need computer vision methods that can operate directly in 3D, but this presents three clear challenges: sensing, representing, and understanding 3D data.

The process of capturing 3D data is complex. While 2D cameras are inexpensive and widespread, specialized hardware setups are typically needed for 3D sensing.

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Stereo vision uses multiple cameras and measures the shift in perceived object position to compute depth information (Source: University of Edinburgh)

  1. Stereo fixes two or more cameras at specific positions relative to one another and uses this setup to capture different images of a scene, match the corresponding pixels, and compute how each pixel’s position differs between the images to calculate its position in 3D space. This is roughly how humans perceive the world — our eyes capture two separate "images" of the world, and then our brains look at how an object's position differs between the views from our left and right eyes to determine its 3D position. Stereo is attractive because it involves simple hardware — just two or more ordinary cameras. However, the approach isn't great in applications where accuracy or speed matter, since using visual details to match corresponding points between the camera images is not only computationally expensive but also error-prone in environments that are textureless or visually repetitive.

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RGB-D cameras output a four-channel image that contains color information along with per-pixel depth (Source: Kyushu University)

  1. RGB-D involves using a special type of camera that captures depth information (“D”) in addition to a color image (“RGB”). Specifically, it captures the same type of color image you'd get from a normal 2D camera, but, for some subset of the pixels, also tells you how far in front of the camera the object is. Internally, most RGB-D sensors work through either “structured light,” which projects an infrared pattern onto a scene and senses how that pattern has warped onto the geometric surfaces, or “time of flight,” which looks at how long projected infrared light takes to return to the camera. Some RGB-D cameras you might have heard of include the Microsoft Kinect and the iPhone X's Face ID sensor. RGB-D is great because these sensors are relatively small and low-cost but also fast and immune to visual matching errors. However, RGB-D cameras often have many holes in their depth output due to occlusion (where objects in the foreground block projection onto objects behind them), pattern sensing failures, and range issues (both projection and sensing becomes difficult further away from the camera).

LIDAR uses several laser beams (sensing in concentric circles) to directly perceive the geometric structure of an environment (Source: Giphy)

  1. LiDAR involves firing rapid laser pulses at objects and measuring how much time they take to return to the sensor. This is similar to the "time of flight" technology for RGB-D cameras we described above, but LiDAR has significantly longer range, captures many more points, and is much more robust to interference from other light sources. Most 3D LiDAR sensors today have several (up to 64) beams aligned vertically, spinning rapidly to see in all directions around the sensor. These are the sensors used in most self-driving cars because of their accuracy, range, and robustness, but the problem with LiDAR sensors is that they're often large, heavy, and extremely expensive (the 64-beam sensor that most self-driving cars use costs $75,000!). As a result, many companies are currently trying to develop cheaper “solid state LiDAR” systems that can sense in 3D without having to spin.

3D Representations

Once you've captured 3D data, you need to represent it in a format that makes sense as an input to the processing pipeline you’re building. There are four main representations you should know:

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The different representations of 3D data. (a) point cloud (source: Caltech), (b) voxel grid (source: IIT Kharagpur), (c) triangle mesh (source: UW), (d) multi-view representation (source: Stanford)

a. Point clouds are simply collections of points in 3D space; each point is specified by an (xyz) location, optionally along with some other attributes (like rgb color). They’re the raw form that LiDAR data is captured in, and stereo and RGB-D data (which consist of an image labeled with per-pixel depth values) are usually converted into point clouds before further processing.

b. Voxel grids are derived from point clouds. "Voxels" are like pixels in 3D; think of voxel grids as quantized, fixed-sized point clouds. Whereas point clouds can have an infinite number of points anywhere in space with floating-point pixel coordinates, voxel grids are 3D grids in which each cell, or “voxel,” has a fixed size and discrete coordinates.

c. Polygon meshes consist of a set of polygonal faces with shared vertices that approximate a geometric surface. Think of point clouds as a collection of sampled 3D points from an underlying continuous geometric surface; polygon meshes aim to represent those underlying surfaces in a way that can be easily rendered. While originally created for computer graphics, polygon meshes can also be useful for 3D vision. There are several methods to obtain polygon meshes from point clouds, including Kazhdan et al.'s Poisson surface reconstruction[1] (2006).

d. Multi-view representations are collections of 2D images of a rendered polygon mesh captured from different simulated viewpoints ("virtual cameras") to convey the 3D geometry in a simple way. The difference between simply capturing images from multiple cameras (like in stereo) and constructing a multi-view representation is that multi-view requires actually building a full 3D model and rendering it from several arbitrary viewpoints to fully convey the underlying geometry. Unlike the other three representations above, which are used for both storage and processing of 3D data, multi-view representations are typically only used to turn 3D data into an easy format for processing or visualization.

Now that you've turned your 3D data into a digestible format, you need to actually build out a computer vision pipeline to understand it. The problem here is that extending traditional deep learning techniques that work well on 2D images (like CNNs) to operate on 3D data can be tricky depending on the representation of that data, making conventional tasks like object detection or segmentation challenging.

Learning with multi-view inputs

Using a multi-view representation of 3D data is the simplest way to adapt 2D deep learning techniques to 3D. It's a clever way to transform the problem of 3D perception into one of 2D perception, but in a way that still allows you to reason about the 3D geometry of an object. The early deep learning-based work to use this idea was Su et al.'s multi-view CNN[2] (2015), a simple yet effective architecture that can learn a feature descriptor from multiple 2D views of a 3D object. Implementing this approach, the method had increased performance compared to using a single 2D image for the object classification task. This is accomplished by feeding in individual images into a VGG network pre-trained on ImageNet in order to extract salient features, pooling these resulting activation maps, and passing this information into additional convolutional layers for further feature learning.

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Multi-view CNN architecture (Source: paper)

Still, multi-view image representations have numerous limitations. The main issue is that you aren't truly learning in 3D — a fixed number of 2D views is still just an imperfect approximation of an underlying 3D structure. As a result, tasks like semantic segmentation, especially across more complex object and scenes, become challenging because of the limited feature information gained from 2D images. Moreover, this form of visualizing 3D data is not scalable for computationally constrained tasks like autonomous driving and virtual reality — keep in mind that generating a multi-view representation requires rendering a full 3D model and simulating several arbitrary viewpoints. Ultimately, multi-view learning is faced with many drawbacks that motivates research on methods that learn directly from 3D data.

Learning with volumetric representations

Learning with voxel grids solves the main drawbacks of multi-view representations. Voxel grids bridge the gap between 2D and 3D vision — they're the closest 3D representation to images, making it relatively easy to adapt 2D deep learning concepts (like the convolution operator) to 3D. Maturana and Scherer's VoxNet[3] (2015) was one of the first deep learning methods to achieve compelling results on the object classification task given a voxel grid input. VoxNet operates on probabilistic occupancy grids, in which each voxel contains the probability that that voxel is occupied in space. A benefit of this approach is that it allows the network to differentiate between voxels that are known to be free (e.g., voxels a LiDAR beam passed through) and voxels whose occupancy is unknown (e.g., voxels behind where a LiDAR beam hit).

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VoxNet architecture (Source: paper)

VoxNet's architecture itself is fairly straightforward, consisting of two convolutional layers, a max pooling layer, and two fully-connected layers to compute an output class score vector. This network is much shallower and has far fewer parameters than most state-of-the-art image classification networks, but it was selected from a stochastic search over hundreds of possible CNN architectures. Since voxel grids are so similar to images, the actual strided convolution and pooling operators they employ are trivial adaptations of these operators from working on 2D pixels to 3D voxels; the convolution operator uses a $d \times d \times d \times c$ kernel rather than the $d \times d \times c$ kernel used in 2D CNNs, and the pooling operator considers non-overlapping 3D blocks of voxels rather than 2D blocks of pixels.

One issue with VoxNet is that the architecture isn't inherently rotation-invariant. While the authors reasonably assume that the sensor is kept upright so that the $z$ axis of the voxel grid is aligned with the direction of gravity, no such assumptions can be made about rotation about the $z$ axis — an object from behind is still that same object, even though the geometry in the voxel grid would interact with the learned convolution kernel very differently. To solve this, they employ a simple data augmentation strategy. During training, they rotate each model several times and train on all copies; then, at test time, they pool the output of the final fully-connected layer across several rotations of the input. They note that this approach led to similar performance but faster convergence compared to pooling the output of intermediate convolutional layers like Su et al.'s multi-view CNNs do in their "view pooling" step. In this way, VoxNet learns rotation invariance by sharing the same learned convolutional kernel weights across different rotations of the input voxel grid.

VoxNet represents a huge step towards true 3D learning, but voxel grids still have a number of drawbacks. First, they lose resolution compared to point clouds, since several distinct points representing intricate structures will be binned into one voxel if they're close together. At the same time, voxel grids can lead to unnecessarily high memory usage compared to point clouds in sparse environments, since they actively consume memory to represent free and unknown space whereas point clouds contain only known points.

Learning with point clouds

In light of these issues with voxel-based approaches, recent work has focused on architectures that operate directly on raw point clouds. Most notably, Qi et al.'s PointNet[4] (2016) was one of the first proposed methods for handling this form of irregular 3D data. However, as noted by the authors, point clouds are simply a set of points represented in 3D by their xyz locations. More concretely, given $N$ points in a point cloud, the network needs to learn unique features that are invariant to the $N!$ permutations of this input data, since the ordering of points fed into a network doesn't impact the underlying geometry. In addition, the network should be robust to transformations of the point cloud — rotations, translations and scaling should not impact prediction.

To ensure invariance across input ordering, the key insight behind PointNet is using a simple symmetric function that produces a consistent output for any ordering of the input (examples in this class of functions include addition and multiplication). Guided by this intuition, the basic module behind the PointNet architecture (called PointNet Vanilla) is defined as follows:

$$f(x_1, \ldots, x_n) = \gamma \odot g(h(x_1), \ldots, h(x_n))$$

where $f$ is a symmetric function that transforms input points into a $k$-dimensional vector (for object classification). This function $f$ can be approximated such that there exists another symmetric function $g$. In the equation, $h$ is a multi-layer perceptron (MLP) that maps individual input points (and their corresponding features such as xyz position, color, surface normals, etc.) into a higher dimensional latent space. The max-pooling operations serves as the symmetric function $g$, aggregating learned features into a global descriptor for the point cloud. This single feature vector is passed into $\gamma$, another MLP that outputs predictions for objects.

To address the challenge of learning representations that are invariant to geometric transformations of the point cloud, PointNet employs a mini-network, called T-Net, which applies an affine transformation onto the input point cloud. The concept is similar to Jaderberg et al.'s spatial transformer networks[5] (2016) but is much simpler because there is no need for defining a new type of layer. T-Net consists of learnable parameters that enable PointNet to transform the input point cloud into a fixed, canonical space — ensuring that the overall network is robust to even the slightest of variations.

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PointNet architecture (Source: paper)

The overall PointNet architecture integrates the vanilla approach and T-Net with multiple MLP layers that create feature representations for the point clouds. However, beyond just object classification, PointNet also enables semantic segmentation of both objects and scenes. To accomplish this, the architecture combines the global feature vector from the max pooling symmetric function with per-point features learned after the input data is passed through a few MLPs. By concatenating these two vectors, each point is aware of both its global semantics and its local features, enabling the network to learn additional, more meaningful features that help with segmentation.

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Semantic segmentation results of indoor scenes using PointNet (Source: paper)

Despite the impressive results with PointNet, one of the primary drawbacks is that the architecture fails to capture the underlying local structure within neighborhoods of points — an idea that is similar to extracting features from increasing receptive field sizes in images using CNNs. To address this issue, Qi et al. developed PointNet++[6] (2017), a spinoff from the PointNet architecture but is also capable of learning features from local regions within a point cloud. The basis behind this method is a hierarchical feature learning layer that has three key steps. It (1) samples points to serve as centroids for the local region, (2) groups neighboring points in these local regions based on distance from the centroid, and (3) encodes features for these regions using a mini-PointNet.

These steps are progressively repeated so that features are learned across varying sizes of point groups within the point cloud. In doing so, the network can better understand the underlying relationships within local clusters of points across the entire point cloud — ultimately aiding in improved generalization performance. The results of this work demonstrate that PointNet++ enables significant improvements over existing methods including PointNet, and achieved state-of-art performance on 3D point cloud analysis benchmarks (ModelNet40 and ShapeNet)

Promising new research areas

Graph CNNs

Current research on deep learning architectures for processing 3D data has been focused on point cloud representations, with much of the recent work extending ideas from PointNet/PointNet++ and drawing inspiration from other fields to further improve performance. An example of one such paper is Wang et al.'s Dynamic Graph CNNs[7] (2018), which uses graph-based deep learning methods to improve feature extraction in point clouds. The idea is that PointNet and PointNet++ fail to capture the geometric relationships among individual points because these methods need to maintain invariance to different input permutations. However, by considering a point and it's surrounding nearest neighbors as a directed graph, Wang et al. construct EdgeConv, an operator that generates unique features across points in the data. Read another Gradient overview if you are interested in learning more about learning on graphs.

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SPLATNet architecture (Source: paper)

On the other hand, some research has taken a step away from the classic feature extraction methods proposed in PointNet/PointNet++, opting to design a new method for processing point clouds all together. Su et al.'s SPLATNet (2018)[8] architecture is a great example of this new focus in point cloud research — the authors design a novel architecture and convolution operator than can directly operate on point clouds. The key insight behind this paper is translating the concept of "receptive fields" to irregular point clouds, which enables spatial information to be preserved even across sparse regions (a key drawback with PointNet/PointNet++). What's especially fascinating is that SPLATNet can project features extracted from multi-view images into 3D space, fusing this 2D data with the original point cloud in an end-to-end learnable architecture. Using this 2D-3D joint learning, SPLATNet achieved a new state-of-the-art in semantic segmentation.

Frustum PointNets

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Visualizing a 3D frustum generated from a 2D bounding box estimation (Source: paper)

A third promising reserach direction involves extending the basic architectural building blocks we described above to build more elaborate networks for useful tasks like object detection in 3D. Building on the idea of using both 2D and 3D data, Qi et al.’s Frustum PointNets[9] (2017) presents a new approach that fuses RGB images with point clouds to improve the efficiency of localizing objects in large 3D scenes. Conventional approaches for this task determine possible 3D bounding boxes for objects by performing classification on sliding windows directly over the entire point cloud, which is computationally expensive and makes real-time prediction difficult. Qi et al. make two key contributions.

First, they propose to initially use a standard CNN for object detection on 2D images, extrude a 3D frustum corresponding to the region of the point cloud in which the detected object could reside, and then perform the search process over only this "slice" of the point cloud. This significantly narrows down the search space for bounding box estimation, reducing the likelihood of false detections and considerably speeding up the processing pipeline, which is crucial for autonomous driving applications.

Second, instead of performing the typical sliding window classification to during the bounding box search process, Qi et al. design a novel PointNet-based architecture that can directly perform instance segmentation (segmenting the point cloud into individual objects) and bounding box estimation over the entire 3D frustum in one pass, making their method both fast and robust to occlusions and sparsity. Ultimately, as a result of these improvements, this work outperformed all prior approaches at the time of publication on the KITTI and SUN RGB-D 3D detection benchmarks.

Final thoughts

Over just the past 5 years, 3D deep learning methods have progressed from working with derived (multi-view) to raw (point cloud) representations of 3D data. Along the way, we've moved from methods that were simple adaptations of 2D CNNs to 3D data (multi-view CNNs and even VoxNet) to methods that were handcrafted for 3D (PointNet and other point cloud methods), greatly improving performance on tasks like object classification and semantic segmentation. These results are promising because they confirm that there truly is value in seeing and representing the world in 3D.

However, advancements in this field have just begun. Current work focuses not only on improving the accuracy and performance of these algorithms, but also on ensuring robustness and scalability. And although much of the current research is motivated by autonomous vehicle applications, new methods operating directly on point clouds will play a significant role in 3D medical imaging, virtual reality, and indoor mapping.

(Cover image: Waymo)

Mihir Garimella and Prathik Naidu are rising sophomores at Stanford University majoring in Computer Science. They’ve done research in robotics, computer vision, and machine learning and have industry experience at Facebook’s Connectivity Lab and Amazon’s CoreAI team, respectively. They're the co-founders of Firefly Autonomy, an early-stage startup building autonomous drones for indoor mapping and industrial inspection.

  1. Kazhdan, Michael, and Hugues Hoppe. "Screened poisson surface reconstruction." ACM Transactions on Graphics (ToG) 32.3 (2013): 29. ↩

  2. Su, Hang, et al. "Multi-view convolutional neural networks for 3d shape recognition." Proceedings of the IEEE international conference on computer vision. 2015. ↩

  3. Maturana, Daniel, and Sebastian Scherer. "Voxnet: A 3d convolutional neural network for real-time object recognition." Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on. IEEE, 2015. ↩

  4. Qi, Charles R., et al. "Pointnet: Deep learning on point sets for 3d classification and segmentation." Proc. Computer Vision and Pattern Recognition (CVPR), IEEE 1.2 (2017): 4. ↩

  5. Jaderberg, Max, Karen Simonyan, and Andrew Zisserman. "Spatial transformer networks." Advances in neural information processing systems. 2015. ↩

  6. Qi, Charles Ruizhongtai, et al. "Pointnet++: Deep hierarchical feature learning on point sets in a metric space." Advances in Neural Information Processing Systems. 2017. ↩

  7. Wang, Yue, et al. "Dynamic graph CNN for learning on point clouds." arXiv preprint arXiv:1801.07829 (2018). ↩

  8. Su, Hang, et al. "Splatnet: Sparse lattice networks for point cloud processing." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. ↩

  9. Qi, Charles R., et al. "Frustum pointnets for 3d object detection from rgb-d data." arXiv preprint arXiv:1711.08488 (2017). ↩


Numerology Number 24

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The Meaning of Number 24

In traditional Numerology, when we evaluate multi-digit numbers like 24, we do so by reducing them to the root numbers that ultimately inform their meaning.

Each root number, those being the numbers 0-9, have their own vibrational meaning. Multi-digit or compound numbers are viewed as combinations of these energies.

Deepak Chopra once said, “The universe is made up fundamentally of energy and information.” In the view of modern physics, everything that appears in the material world, including our thoughts, begins in what is called a Unified Field of energy and information.

Numbers, of course, simply represent ways that information can be organized.

The meanings that accompany the numbers that enter our experience are not impressed upon them by our conscious minds, but rather come from that field of great depth often referred to in spiritual traditions as spirit, source energy or soul.

When a numerologist analyzes your Numerology chart, what they are doing is deciphering the clues that come from source energy or soul about what will bring meaning, purpose and fulfillment to your life.

The Numerological Meaning of 24

The number 24 can be reduced to its root number by simply adding its individual digits together, like this: 2 + 4 = 6

This means that the characteristics that make up number 24’s signature vibration will include the vibrational essence of 2, 4 and 6. When these numbers combine, the focus is on teamwork, cooperation, family, domestic life and harmonious relationships.

Special Properties of 24

The number 24 itself has some very interesting properties that seem to support this numerological meaning. First of all, in mathematics, 24 is a highly composite number. This is because it has more divisors or ‘members of its factor set’ than any other two digit number. In all, 8 different numbers combine to produce 24.

24 is also the exact number of times you can rearrange a set of 4 different items. Four is the number of completion, stability and has highly material connotations.

There are the four cardinal directions, the four corners of a structure or foundation, the four priomordial elements (fire, air, water and earth), the four rivers of creation and the four winds that blow from each direction.

When you take the 4 elements of creation and multiply them by the 6 senses (4 X 6 = 24) the result is human experience which is shaped by the 24 hours of the day.

The Deeper Symbolic Meaning of 24

Human experience, as James Joyce notes in his ground breaking novel Ulysses, is shaped by the a priori forms of sensibility — namely time and space. Joyce’s great novel is a cosmos condensed into a single day.

James Joyce’s Ulysses was inspired by the ancient tale of the hero, Homer’s Odyssey, which, it is no accident, is made up of 24 episodes that cover the 20 year return journey that Odysseus took finding his way home to his wife Penelope and son Telemachus.

As Joseph Campbell, the notable scholar of Myth and Literature has noted, the story of Joyce’s hero in Ulysses is the story of every person who ventures out into the world, only to round their day with a return journey home again.

Number 24 and the Cosmic Cycle

The 24th kua of the I Ching is usually translated Return, but is also sometimes translated as The Turning Point, and its image is thunder (CHEN) in the bowels of the earth (K’UN). In the Wilhelm/Baynes translation, the oracle states, “Return. Success… To and Fro goes the Tao (way).

On the seventh day comes the return.” Then the sublime commentary by Confucius brings it all together: “It is in the Return cycles that the very heart of the workings of heaven and earth become apparent.”

Like thunder buried in the bowels of the earth, each December 24th Christians endure the longest, darkest night of the year, the Winter solstice, knowing that the savior will be born the following morning.

The number 24 is indicative of the deep cycles of the heart that enclose us into systems of sentiment and belief that are so powerful they form the foundations of all societies.

The Challenge of Number 24

If taken as Tarot cards, the 2 and the 4 of 24 would appear as the introverted High Priestess and the highly materialistic Emperor. When you combine these two numbers you end up at station 6 of the Tarot, The Lovers.

The Lovers is indicative of the need to make a decision, and that is the challenge of the energy of number 24. In most cases, the main decision that people have to make is between economic and domestic considerations.

When the number 24 appears in your experience, as a date, a time (12 midnight in military time), in an address or as an age, it may indicate a message from source to take domestic matters into consideration. Often we get lost in our work lives and forget about how important it is to take that return journey home.

Number 24 as a Birth Number

Your birth number is 24 when you are born on the 24th of any month. 24 has a vibrational energy like that of birth numbers 6 and 15, but because of its special properties (noted above) 24 has a vibrational essence that is especially associated with relationships, family and companionship.

Like those who are born with the essence of number 6 in their Numerology chart, those with a birth number of 24 are likely to gravitate to occupations associated with the home, with a special emphasis on relationships.

Number 24 the Diplomat

Like the image of thunder in the bowels of the earth from the I Ching suggests, there is great power within someone who has the unique energy of number 24 in their Numerology chart.

This power usually emerges in the stabilizing and diplomatic influence that they will have over a family.

The essence of 24 can manifest in your occupation as a healer or counselor, a security officer keeping people safe, an artist who is creative and nurturing or in a domestic role in which care and nurturing are evident.

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Great Lakes

Increases in extreme precipitation events, changes in growing seasons, and warming temperatures impact the people, ecosystems, and infrastructure of the Great Lakes region—and the lakes themselves. Given its energy-intensive economy, the region has the potential to reduce emissions that cause climate change and adapt to the impacts already being experienced.

    Key Points:

  • Extreme rainfall events have increased over the last century, and these trends are expected to continue. Combined with land cover changes, increased precipitation has and likely will continue to lead to flooding, erosion, declining water quality, and negative impacts on transportation, agriculture, human health, and infrastructure.
  • In the next few decades, longer growing seasons and rising carbon dioxide levels will increase yields of some crops; those benefits will be progressively offset by extreme weather events. In the long term, the combined stresses associated with climate change are expected to decrease agricultural productivity.
  • The composition of forests in the Great Lakes is changing as the climate warms. Many tree species are shifting northward, with more southerly varieties replacing them. Many iconic north wood tree species will lose their advantage and be slowly replaced over the next century.
  • Increased heat wave intensity and frequency, increased humidity, degraded air quality, reduced water quality, and change in vector-borne disease patterns will increase public health risks.
  • The Great Lakes region has a highly energy-intensive economy, with per capita emissions of greenhouse gases more than 20 percent higher than the national average. The region also has a large and increasingly utilized potential to reduce emissions that cause climate change.
  • Climate change will exacerbate a range of risks to the Great Lakes, including changes in the range and distribution of some species, increases in invasive species and harmful blooms of algae, and declines in beach health.
  • Ice cover declines will lengthen the commercial navigation season.
  • At-risk communities in the Great Lakes are becoming more vulnerable to climate change impacts such as flooding, drought, and increases in urban heat islands. Tribal nations are especially vulnerable because of their reliance on threatened natural resources for their cultural, subsistence, and economic needs. Integrating climate adaptation into planning processes offers an opportunity to better manage climate risks now. Developing knowledge for decision making in cooperation with vulnerable communities and tribal nations will help to build adaptive capacity and increase resilience.

The Great Lakes region includes all or portions of eight U.S. states—Minnesota, Wisconsin, Michigan, Indiana, Illinois, and Ohio in their entirety, plus the Great Lakes watershed areas of Pennsylvania and New York—as well as the province of Ontario, Canada. The region straddles U.S. regions typically described as the Midwest and the Northeast.

Map of the Great Lakes region

While Ontario is generally included in the definition of the Great Lakes region, this website focuses on climate resilience in the United States and is intended to serve the U.S. portion of the Great Lakes region.

The region is home to approximately 23 million people on the U.S. side of the Great Lakes Basin, and contains agricultural lands, forests, urban areas (including Buffalo, Chicago, Cleveland, Detroit, Milwaukee, and Rochester), diverse shorelines, and the Great Lakes themselves—the region’s economic backbone.

Scenes from the Great Lakes: Agriculture, Forests, Cities, Shorelines, and the Lakes Themselves

The Great Lakes region includes agricultural lands, forests, urban areas, diverse shorelines, and—of course—the Great Lakes themselves

Together, the Great Lakes make up the largest freshwater system on Earth, containing 84 percent of North America’s fresh surface water. The lakes are a vital resource for water supply, transportation, recreation, and power generation, among other uses.

In recent decades, the region has experienced substantial shifts in populations, socioeconomics, air and water pollution, and land use. Both the built and the natural environment display potential vulnerabilities to climate variability and change.

Regional impacts of a changing climate

Climate change will tend to amplify existing risks that impact people, ecosystems, and infrastructure. The effects of increased heat stress, flooding, drought, and late spring freezes may be magnified by other changes, such as a change in the prevalence of diseases and pests, increased competition from invasive species, land use change, an increase in air pollution, and economic shocks from extreme weather events. These impacts are particularly concerning to the region because a major component of the regional economy relies on the fisheries, recreation, tourism, and commerce generated by the Great Lakes and the northern forests.

Temperatures in the Great Lakes region have been rising over the past several decades. The average temperature in northern portions of the region has increased by more than 1.5°F compared to the 1901–1960 average, and the rate of warming has increased in the last decade. Temperatures in the winter and at night are warming faster than in other seasons or in the daytime.

Maps showing observed changes in temperatures across the United States

Observed changes in annual, winter, and summer temperature in the United States (°F). Changes are the difference between the average for present-day (1986–2016) and the average for the first half of the last century (1901–1960 for the contiguous United States, 1925–1960 for Alaska and Hawai‘i).

The amount of future warming for the region will depend on the amount of heat-trapping gases (emissions from burning fossil fuels) in the atmosphere. Regional projections for the middle of the 21st century suggest warming of 3.5–4.5°F above the 20th century average in a lower emissions scenario and 5.5–6.5°F in a higher emissions scenario. By the end of the century, projections indicate warming from approximately 5.5–6.5°F (lower emissions scenario) to 7.5–9.5°F (higher emissions scenario).

Map of the United States divided into regions showing percentage change on heavy precipitation between 1958 and 2016

The map shows percent increases in the amount of precipitation falling in daily events that exceed the 99th percentile of all non-zero precipitation days (top 1 percent of all daily precipitation events) from 1958 to 2016 for each region of the United States. The number in each black circle is the percent change over the entire period (1958–2016).

Annual precipitation in the Great Lakes region has generally increased over the past several decades. Some of this increase is attributable to increases in the intensity and duration of the heaviest rainfalls—a trend that is projected to continue into the future. Observations have not documented any change in drought duration in the region (or the larger Midwest region) over the past century.

Model projections for future precipitation are less certain than those for temperatures. By late this century, under a higher emissions scenario, models project average winter and spring precipitation to increase 10 to 20 percent, relative to 1970–2000; under the same conditions, changes in summer and fall precipitation are not projected to be larger than natural variations. Some regional climate model projections using the same emissions scenarios also project increased spring precipitation and decreased summer precipitation, though these expected patterns are not as significantly altered as they are to the south of the Great Lakes. Increases in the frequency and intensity of extreme precipitation are projected across the Great Lakes region.

Lake-effect snow

Satellite image of lake-effect snow occurring over the Great Lakes region

Satellite image of lake-effect snow occurring over the Great Lakes region. Lake-effect snow occurs when cold dry air passes over a large, warm lake and picks up moisture and heat. Clouds build overhead and eventually develop into snow showers as they move downwind. In this image, the wind is moving from the northwest to the southeast. It picks up moisture from Lakes Nipigon, Superior, and Michigan and deposits it as snow further downwind.

In the snow belts of the Great Lakes, average lake-effect snowfall activity has increased since the early twentieth century for Lakes Superior, Michigan-Huron, and Erie. Individual studies for Lakes Michigan and Ontario, however, indicate that this increase has not been continuous—in both cases, upward trends were observed until the 1970s and early 1980s; since then, lake-effect snowfall has decreased in these regions.

Ice cover extent and lake water temperatures are the main controls on lake-effect snow that falls downwind of the Great Lakes. As the region warms and ice cover diminishes in winter, models predict that more lake-effect snow will occur. The predictions change once lake temperatures rise to a point when much of what now falls as snow will instead fall as rain.

The Michigan Department of Transportation has noted that an increase in lake-effect snows means that existing models used for snow and ice removal procedures are no longer reliable. Changing conditions will require better monitoring, new models, and improvements in roadway condition detection systems.

The preceding text is excerpted and abridged from the Synthesis of the Third National Climate Assessment for the Great Lakes Region, the Climate Science Special Report: Fourth National Climate Assessment, Volume I (Section 7.1.2), and the U.S. National Climate Assessment: Climate Change Impacts in the United States (Chapter 5, Transportation).

Increased risks to the Great Lakes

The Great Lakes themselves influence regional climate conditions and impact climate variability and change. They also influence daily weather by (1) moderating maximum and minimum temperatures, (2) increasing cloud cover and precipitation over and just downwind of the lakes during winter, and (3) decreasing summertime convective clouds and rainfall over the lakes. Consistent with observed changes in regional climate, the Great Lakes show a trend toward higher water temperatures and smaller average winter ice extents.

Water temperatures

Maps showing the rate of change of summer surface water temperature in the Great Lakes

Summer surface water temperature increased in most areas of the Great Lakes between 1994 and 2013; the map shows the rate of change. The greatest rates of increase occurred in deeper water, with smaller increases occurring near shorelines.

Summer surface water temperature in Lake Huron increased 5.2°F between 1968 and 2002. Over the same period, summer surface water temperature in Lake Superior increased 4.5°F, twice the rate of increase in air temperature. These lake surface temperatures are projected to rise by as much as 7°F by 2050 and 12.1°F by 2100.

Higher temperatures, increased precipitation, and lengthened growing seasons are likely to result in increased production of blue-green and toxic algae in the lakes. Blooms of these algae can harm fish, water quality, habitats, and aesthetics—and could worsen the impact of invasive species.

Ice coverage

Maps showing the duration of seasonal ice cover in the Great Lakes

Although there is substantial variability from year to year, the average annual maximum ice coverage in the Great Lakes was just 40 percent between 2003 and 2012—smaller than the average 52 percent coverage from 1962 to 2013. For comparison, maximum ice coverage averaged 67 percent during the 1970s, a decade that included several extremely cold winters. From 1973 to 2010, ice cover on the Great Lakes declined an average of 71 percent, although it was high in the winters of 2014 and 2015. Less ice, coupled with more frequent and intense storms, leaves shores vulnerable to erosion and flooding and could harm property and fish habitat.

Reduced ice cover also has the potential to lengthen the shipping season. The navigation season increased by an average of eight days between 1994 and 2011: extra days for shipping can benefit commerce, but navigable conditions can also increase shoreline scouring and bring in more invasive species.

Lake levels and destratification

The cargo freighter SS Arthur M. Anderson

The cargo freighter SS Arthur M. Anderson.

Water levels in the Great Lakes fluctuate naturally, and it is more likely than not that levels will decline with a changing climate. Changes in lake levels can influence the amount of cargo that can be carried through them on ships. On average, a 1,000-foot ship sinks one inch lower in the water for every 270 tons of cargo it carries. If a ship is currently limited by water depth, any lowering of lake levels will result in a reduction in the amount of cargo it can transport to Great Lakes ports.

In addition, fluctuating lake levels influence how people use and interact with shorelines. During sustained low water periods, development marches closer to the lake, shorefront landowners “groom” emergent coastal wetlands, and increased navigational dredging is needed. When lake levels go up, erosion and flooding, particularly when coupled with storms, can threaten properties and public safety.

It’s unclear whether, or how much, a changing climate will affect lake levels in the future. Current estimates of lake level changes are uncertain; recent studies, along with a large spread in existing modeling results, indicate that projections of Great Lakes water levels represent evolving research and are still subject to considerable uncertainty.

An important seasonal event for biological activity in the lakes is the turnover of water, or destratification. This has historically occurred twice a year: during the fall as water temperature drops below 39°F, the point at which freshwater attains its maximum density, and again in the spring, when water temperature rises above that threshold. The mixing that occurs during these times carries oxygen down from the surface and nutrients up from the bottom. Climate projections suggest that the overturn in spring, which triggers the start of the aquatic "growing season," will happen earlier and that the fall overturn will happen later. As the duration of the stratified period increases, the risk of impacts from low oxygen levels at depth and lack of nutrient at the surface increases, which can potentially lead to declines in species populations in both zones.

The preceding text is excerpted and abridged from the reports Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume IIChapter 21: Midwest and Synthesis of the Third National Climate Assessment for the Great Lakes Region.

Note on regional modeling uncertainties

Interaction between the lakes and the atmosphere in the Great Lakes region (e.g., through ice cover, evaporation rates, moisture transport, and modified pressure gradients) is crucial to simulating the region’s future climate (i.e., changes in lake levels or regional precipitation patterns). Globally recognized modeling efforts (i.e., the Coupled Model Intercomparison Project, or CMIP) do not include a realistic representation of the Great Lakes, simulating the influence of the lakes poorly or not at all. Ongoing work to provide evaluation, analysis, and guidance for the Great Lakes region includes comparing this regional model data to commonly used global climate model data (CMIP) that are the basis of many products practitioners currently use (i.e., NCA3IPCCNOAA State Climate Summaries). To address these challenges, a community of regional modeling experts are working to configure and utilize more sophisticated climate models that more accurately represent the Great Lakes’ lake–land–atmosphere system to enhance the understanding of uncertainty to inform better regional decision-making capacity. For more information, visit the GLISA's Great Lakes Ensemble page.

Learn more

To learn more about the impacts of climate change and variability and building climate resilience in the Great Lakes, visit these pages:

Banner Image Credit
Grand Haven, Lake Michigan. Image credit: D. O'Keefe, Michigan Sea Grant
Last modified
10 May 2019 - 11:04am


about prostate cancer

10 Vitamin D Deficiency Symptoms You Can Identify Yourself

Vitamin D Deficiency Symptoms Vitamin D deficiency symptoms have been linked to numerous health problems, including heart disease, depression, and even cancer.[1] Here are 10 signs you're not getting enough vitamin D: Depression or anxiety Bone softening (low bone density) or fractures Fatigue and generalized weakness Muscle cramps and weakness … Read More

12 Risk Factors for Prostate Cancer

Can You Die from Prostate Cancer?

Before widespread prostate cancer screening began in the late 1980s, many men newly diagnosed with the disease had advanced cancer that had spread beyond the prostate. Today, in the screening era, most men with the disease are diagnosed when their cancer is in an earlier, more curable stage. But, even … Read More

Are You Keeping Tabs on Your PSA Levels?

Three big letters: P-S-A. In the world of urology and men’s health, perhaps no three letters generate more controversy. Since the early 1990s, the prostate-specific antigen (or PSA) blood test has served as the cornerstone of prostate cancer early detection. Today, it remains at the center of a debate over … Read More

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Friendship and Autism

Understanding High Functioning Autism, From the Heart of an Autism Mom, Part 1

Friendship and Autism

So, I will be doing a series of articles, about the struggles of those of us with High Functioning Autism. I really want to encourage you to share, to grow the awareness, and promote acceptance. As my first blog article stated, I am as Aspie. (See the link at the end) My son, who is 15 years old, is also HFA. He is my inspiration for what I am writing today, though I see a lot of it in myself, as well.

Today, I want to share an issue that plagues so many HFA children, teens, and adults. Friendships. Everyone wants a friend, right? Especially our teenagers. It is a rite of passage, and a way to feel validated. I am going to use my favorite and most affectionate term, Aspie, as I write.

Unfortunately, for many tweens and teens on the spectrum, making friends is difficult. Holding onto those friends is even more so. Let me explain, with just a few examples.

Aspies are not always obvious. They appear to be normal, from a distance. Sometimes, they appear simply stuck up, or overly shy. Eventually, a kind souled person, usually someone who is also on the outside of polite society, sometimes a person who simply feels empathetic, will make a connection, and start a friendship. Typically, a shared fascination or hobby bonds them.

The connection means the world to an Aspie. Some might not say it, or show it, while others may do so in an almost overwhelming and smothering way. A few of those connections end, at this point.

Others move forward. Unfortunately, problems inevitably arise.

Aspies get zoned in on a particular topic, say, video games, and they forget that the other person does not share such a locked fixation. They have other interests, they would like to share, but the Aspie does not “see” that.

Then there are other Aspies that don’t talk much at all. The friend tries to make conversation, but gets uh-huh, and maybe grunts, and that is all. This can stem from discomfort on the Aspie’s part, or just the inability to find the words to make small talk.

Another Aspie trait, that can be taken wrong by his friends, is the inability to use sarcasm appropriately, or at appropriate times. Oh, many times, I have had my own son, throw out some sarcasm at inappropriate times. Conversations can be very interesting, with my Aspie.

On the other side of sarcasm, the receiving end, a lot of Aspies don’t understand it and take offense. Then, of course, you have an argument. The friend doesn’t understand what just happened, and will sometimes back away, for a time.

Of course, then you have the Social Anxiety angle. A lot of Aspie’s are just not comfortable going out, whether it be a party, the mall, the park, wherever. There are sounds, sights, smells and feelings that cause discomfort. If an Aspie does go out, and they have a panic attack, it is hard for them to recover, quickly. So, they choose to limit outside excursions as much as possible. Friends? They don’t always understand this.

So, eventually, those friendships start to strain under the differences the kids have. Typically, the friend will start spending more time with other kids, and less time with his Aspie friend. Eventually, the friendship comes to an end, leaving the Aspie feeling completely worthless and lost, wondering why they are not liked. In most cases, in my son’s case, they bury themselves into their favorite hobby; gaming, YouTube, whatever it is they are currently fixated on.

Knowing this, and experiencing it first hand, I offer this advice:

If you or your child have a friend that is a little different, or you see a child all alone, be kind, and consider that they may be an Aspie. Think about how you could help them feel comfortable, and try to understand who they are. Once they trust you, you will see a small change, and once you understand their ways, you will see that they are an extremely fun, and valuable friend. Maybe the best friend you will ever know in your lifetime.

God Bless you!

**For more information about how I came to terms with my Aspieness, check out this article.

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Jenna Logan
Jenna Logan
Read next: Best Running Shoes for Women
Jenna Logan

Christian, ASD mom, and Published Author.

Jesus, my son, and Autism are my life's passions.

Contact me at [email protected]

See all posts by Jenna Logan


://Overview Of Extraction Thimbles

Overview Of Extraction Thimbles

The extraction thimble is a type of cylindrical element for filtration. Generally, it is divided into filtering cartridges as gas and a liquid medium.

Thimbles of liquid filtering medium are usually installed inside the pipeline. Besides, featuring a conical shape of the filter cartridge is usually, they are also named conical filter cartridge.

Common filter materials of extraction thimble involve wood pulp fiber paper and polyester non-woven fabric, among which the later one can process coating, oil-proof, water-proof and static-proof methods.

Installation Method

There are vertical lifting, inclined installation(according to specific angles) and flange installation .etc.

1. Improve the effective filtering area on a large scale.
2. Confirm a low and stable pressure difference and increase the circulation of air volume.
3. The compact filter element is easy to be installed.
4. Especially suitable for industries of high dust concentration.

The size of the extraction thimble is frequently referred to overseas imported samples. Hence, the common specifications are 12.75′, 13.84′, and 26′ in height, which after conversion to metric are actually325 * 660 mm and 350 * 660 mm.

Cellulose Extraction Thimbles for SoxhletSoxhlet Glass Fiber Extraction Thimbles

2019-04-08T01:58:53+00:00April 8th, 2019|


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Basic Needs of Animals

Animal Classification Learn how animals are classified.

Animal Classification Another cool site to learn about how animals are classified.

Where's the Octopus?  This is a cool video about camouflage.

Mimicry Find out how the king snake mimics the coral snake to protect itself.

Life Cylce of a Butterfly   This site has a video that shows the complete metamorphosis of a butterfly.

Life Cycles Learn about the life cycles of a butterfly, bird, and frog.

Animal Adaptations Discover how animal adaptations help students to meet their basic needs.

Animal Adaptations  Learn about animal adaptations and how they help the animals survive.

Create Your Wild Self! Create fun and exciting animals using existing animal adaptations

Bird Beak Adaptations Learn about the structure of bird beaks and how they help the bird get food.

Bird Feet Adaptations Learn about the structure of bird feet and how they help the animal survive.

Sequencing Life Cylce Stages  Correctly sequence the life cycle stages of a butterfly, frog, flower, and apple tree.